Overview

Dataset statistics

Number of variables12
Number of observations3445751
Missing cells0
Missing cells (%)0.0%
Duplicate rows16865
Duplicate rows (%)0.5%
Total size in memory315.5 MiB
Average record size in memory96.0 B

Variable types

Numeric7
Text5

Alerts

Dataset has 16865 (0.5%) duplicate rowsDuplicates
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
magnitudo is highly overall correlated with significanceHigh correlation
significance is highly overall correlated with magnitudoHigh correlation
tsunami is highly skewed (γ1 = 47.45603279)Skewed
tsunami has 3444223 (> 99.9%) zerosZeros
significance has 163607 (4.7%) zerosZeros
depth has 63714 (1.8%) zerosZeros

Reproduction

Analysis started2024-07-01 09:49:08.506394
Analysis finished2024-07-01 09:50:28.563159
Duration1 minute and 20.06 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

time
Real number (ℝ)

Distinct3428775
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2471236 × 1012
Minimum6.3115335 × 1011
Maximum1.6906289 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:28.659343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.3115335 × 1011
5-th percentile7.1656794 × 1011
Q11.024401 × 1012
median1.2823381 × 1012
Q31.5087006 × 1012
95-th percentile1.6528453 × 1012
Maximum1.6906289 × 1012
Range1.0594756 × 1012
Interquartile range (IQR)4.8429956 × 1011

Descriptive statistics

Standard deviation2.9762923 × 1011
Coefficient of variation (CV)0.23865256
Kurtosis-1.0314874
Mean1.2471236 × 1012
Median Absolute Deviation (MAD)2.4116381 × 1011
Skewness-0.34090832
Sum4.2972773 × 1018
Variance8.8583161 × 1022
MonotonicityIncreasing
2024-07-01T14:50:28.819190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.646264543 × 10124
 
< 0.1%
1.646108994 × 10124
 
< 0.1%
9.4392 × 10113
 
< 0.1%
9.20361823 × 10112
 
< 0.1%
7.309523597 × 10112
 
< 0.1%
7.309458786 × 10112
 
< 0.1%
7.309471246 × 10112
 
< 0.1%
7.309479143 × 10112
 
< 0.1%
7.30947942 × 10112
 
< 0.1%
7.309479584 × 10112
 
< 0.1%
Other values (3428765) 3445726
> 99.9%
ValueCountFrequency (%)
6.31153354 × 10111
< 0.1%
6.311534912 × 10111
< 0.1%
6.311540834 × 10111
< 0.1%
6.311555121 × 10111
< 0.1%
6.311558245 × 10111
< 0.1%
6.311558538 × 10111
< 0.1%
6.311560306 × 10111
< 0.1%
6.31156432 × 10111
< 0.1%
6.311566141 × 10111
< 0.1%
6.311566202 × 10111
< 0.1%
ValueCountFrequency (%)
1.690628938 × 10121
< 0.1%
1.690628146 × 10121
< 0.1%
1.690627216 × 10121
< 0.1%
1.690626976 × 10121
< 0.1%
1.690626852 × 10121
< 0.1%
1.690626816 × 10121
< 0.1%
1.690626699 × 10121
< 0.1%
1.690626164 × 10121
< 0.1%
1.690625393 × 10121
< 0.1%
1.69062531 × 10121
< 0.1%

place
Text

Distinct531130
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:29.107246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length67
Median length62
Mean length29.292777
Min length4

Characters and Unicode

Total characters100935617
Distinct characters150
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317021 ?
Unique (%)9.2%

Sample

1st row12 km NNW of Meadow Lakes, Alaska
2nd row14 km S of Volcano, Hawaii
3rd row7 km W of Cobb, California
4th row11 km E of Mammoth Lakes, California
5th row16km N of Fillmore, CA
ValueCountFrequency (%)
of 3305714
 
15.8%
km 2535408
 
12.1%
california 879763
 
4.2%
alaska 811447
 
3.9%
ca 491702
 
2.3%
w 285248
 
1.4%
ese 250426
 
1.2%
e 241915
 
1.2%
cobb 232631
 
1.1%
wnw 222386
 
1.1%
Other values (28691) 11719370
55.9%
2024-07-01T14:50:29.530746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17530358
17.4%
a 8289995
 
8.2%
o 7009091
 
6.9%
k 4907137
 
4.9%
i 4370976
 
4.3%
f 4318286
 
4.3%
m 3846973
 
3.8%
l 3831250
 
3.8%
n 3800284
 
3.8%
, 3362018
 
3.3%
Other values (140) 39669249
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100935617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17530358
17.4%
a 8289995
 
8.2%
o 7009091
 
6.9%
k 4907137
 
4.9%
i 4370976
 
4.3%
f 4318286
 
4.3%
m 3846973
 
3.8%
l 3831250
 
3.8%
n 3800284
 
3.8%
, 3362018
 
3.3%
Other values (140) 39669249
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100935617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17530358
17.4%
a 8289995
 
8.2%
o 7009091
 
6.9%
k 4907137
 
4.9%
i 4370976
 
4.3%
f 4318286
 
4.3%
m 3846973
 
3.8%
l 3831250
 
3.8%
n 3800284
 
3.8%
, 3362018
 
3.3%
Other values (140) 39669249
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100935617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17530358
17.4%
a 8289995
 
8.2%
o 7009091
 
6.9%
k 4907137
 
4.9%
i 4370976
 
4.3%
f 4318286
 
4.3%
m 3846973
 
3.8%
l 3831250
 
3.8%
n 3800284
 
3.8%
, 3362018
 
3.3%
Other values (140) 39669249
39.3%

status
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:29.662657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.0600151
Min length6

Characters and Unicode

Total characters27772805
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreviewed
2nd rowreviewed
3rd rowreviewed
4th rowreviewed
5th rowreviewed
ValueCountFrequency (%)
reviewed 3238918
94.0%
automatic 206821
 
6.0%
manual 12
 
< 0.1%
2024-07-01T14:50:29.907205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9674475
34.8%
i 3430539
 
12.4%
r 3224825
 
11.6%
v 3224825
 
11.6%
w 3224825
 
11.6%
d 3224825
 
11.6%
a 411444
 
1.5%
t 411428
 
1.5%
u 205722
 
0.7%
m 205722
 
0.7%
Other values (18) 534175
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27772805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9674475
34.8%
i 3430539
 
12.4%
r 3224825
 
11.6%
v 3224825
 
11.6%
w 3224825
 
11.6%
d 3224825
 
11.6%
a 411444
 
1.5%
t 411428
 
1.5%
u 205722
 
0.7%
m 205722
 
0.7%
Other values (18) 534175
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27772805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9674475
34.8%
i 3430539
 
12.4%
r 3224825
 
11.6%
v 3224825
 
11.6%
w 3224825
 
11.6%
d 3224825
 
11.6%
a 411444
 
1.5%
t 411428
 
1.5%
u 205722
 
0.7%
m 205722
 
0.7%
Other values (18) 534175
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27772805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9674475
34.8%
i 3430539
 
12.4%
r 3224825
 
11.6%
v 3224825
 
11.6%
w 3224825
 
11.6%
d 3224825
 
11.6%
a 411444
 
1.5%
t 411428
 
1.5%
u 205722
 
0.7%
m 205722
 
0.7%
Other values (18) 534175
 
1.9%

tsunami
Real number (ℝ)

SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00044344469
Minimum0
Maximum1
Zeros3444223
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:29.999642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02105346
Coefficient of variation (CV)47.477082
Kurtosis2250.0764
Mean0.00044344469
Median Absolute Deviation (MAD)0
Skewness47.456033
Sum1528
Variance0.00044324818
MonotonicityNot monotonic
2024-07-01T14:50:30.081724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 3444223
> 99.9%
1 1528
 
< 0.1%
ValueCountFrequency (%)
0 3444223
> 99.9%
1 1528
 
< 0.1%
ValueCountFrequency (%)
1 1528
 
< 0.1%
0 3444223
> 99.9%

significance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1170
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.009735
Minimum0
Maximum2910
Zeros163607
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:30.186396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median33
Q381
95-th percentile312
Maximum2910
Range2910
Interquartile range (IQR)68

Descriptive statistics

Standard deviation101.63641
Coefficient of variation (CV)1.3732844
Kurtosis7.8921127
Mean74.009735
Median Absolute Deviation (MAD)25
Skewness2.2677209
Sum2.5501912 × 108
Variance10329.961
MonotonicityNot monotonic
2024-07-01T14:50:30.332211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 163607
 
4.7%
19 102393
 
3.0%
26 99265
 
2.9%
12 94934
 
2.8%
30 93288
 
2.7%
22 87397
 
2.5%
15 82901
 
2.4%
39 79131
 
2.3%
10 77096
 
2.2%
35 77068
 
2.2%
Other values (1160) 2488671
72.2%
ValueCountFrequency (%)
0 163607
4.7%
1 76140
2.2%
2 59133
 
1.7%
3 43505
 
1.3%
4 68084
2.0%
5 44653
 
1.3%
6 63513
 
1.8%
7 28312
 
0.8%
8 66326
1.9%
9 32329
 
0.9%
ValueCountFrequency (%)
2910 2
< 0.1%
2841 1
< 0.1%
2820 1
< 0.1%
2790 1
< 0.1%
2760 1
< 0.1%
2676 1
< 0.1%
2505 1
< 0.1%
2495 1
< 0.1%
2469 1
< 0.1%
2397 1
< 0.1%
Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:30.454618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length10
Mean length10.016039
Min length6

Characters and Unicode

Total characters34512778
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowearthquake
2nd rowearthquake
3rd rowearthquake
4th rowearthquake
5th rowearthquake
ValueCountFrequency (%)
earthquake 3361846
96.0%
quarry 38880
 
1.1%
blast 38865
 
1.1%
explosion 29128
 
0.8%
ice 13839
 
0.4%
quake 13839
 
0.4%
mining 2177
 
0.1%
event 1707
 
< 0.1%
other 1706
 
< 0.1%
chemical 314
 
< 0.1%
Other values (24) 759
 
< 0.1%
2024-07-01T14:50:30.712158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6815708
19.7%
e 6786118
19.7%
r 3441788
10.0%
u 3414823
9.9%
q 3414564
9.9%
t 3404362
9.9%
k 3375870
9.8%
h 3363867
9.7%
l 68452
 
0.2%
s 68315
 
0.2%
Other values (18) 358911
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34512778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6815708
19.7%
e 6786118
19.7%
r 3441788
10.0%
u 3414823
9.9%
q 3414564
9.9%
t 3404362
9.9%
k 3375870
9.8%
h 3363867
9.7%
l 68452
 
0.2%
s 68315
 
0.2%
Other values (18) 358911
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34512778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6815708
19.7%
e 6786118
19.7%
r 3441788
10.0%
u 3414823
9.9%
q 3414564
9.9%
t 3404362
9.9%
k 3375870
9.8%
h 3363867
9.7%
l 68452
 
0.2%
s 68315
 
0.2%
Other values (18) 358911
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34512778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6815708
19.7%
e 6786118
19.7%
r 3441788
10.0%
u 3414823
9.9%
q 3414564
9.9%
t 3404362
9.9%
k 3375870
9.8%
h 3363867
9.7%
l 68452
 
0.2%
s 68315
 
0.2%
Other values (18) 358911
 
1.0%

magnitudo
Real number (ℝ)

HIGH CORRELATION 

Distinct933
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7740761
Minimum-9.99
Maximum9.1
Zeros23097
Zeros (%)0.7%
Negative88715
Negative (%)2.6%
Memory size26.3 MiB
2024-07-01T14:50:30.845170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9.99
5-th percentile0.2
Q10.91
median1.46
Q32.3
95-th percentile4.5
Maximum9.1
Range19.09
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation1.2910551
Coefficient of variation (CV)0.72773376
Kurtosis1.6953256
Mean1.7740761
Median Absolute Deviation (MAD)0.64
Skewness0.84296237
Sum6113024.6
Variance1.6668233
MonotonicityNot monotonic
2024-07-01T14:50:30.989958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 85212
 
2.5%
1.1 81829
 
2.4%
1.3 81765
 
2.4%
1.2 76955
 
2.2%
1.6 71650
 
2.1%
1.5 69262
 
2.0%
1 64506
 
1.9%
0.9 57673
 
1.7%
1.7 57007
 
1.7%
1.8 55929
 
1.6%
Other values (923) 2743963
79.6%
ValueCountFrequency (%)
-9.99 617
< 0.1%
-9 29
 
< 0.1%
-5 1
 
< 0.1%
-2.6 2
 
< 0.1%
-2.5 4
 
< 0.1%
-2.2 1
 
< 0.1%
-2 4
 
< 0.1%
-1.9 5
 
< 0.1%
-1.82 1
 
< 0.1%
-1.8 6
 
< 0.1%
ValueCountFrequency (%)
9.1 2
 
< 0.1%
8.8 1
 
< 0.1%
8.6 2
 
< 0.1%
8.4 2
 
< 0.1%
8.3 4
< 0.1%
8.2 6
< 0.1%
8.16 1
 
< 0.1%
8.1 7
< 0.1%
8.09 1
 
< 0.1%
8 7
< 0.1%

state
Text

Distinct858
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:31.230560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length54
Median length53
Mean length9.4404744
Min length3

Characters and Unicode

Total characters32529524
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)< 0.1%

Sample

1st row Alaska
2nd row Hawaii
3rd row California
4th row California
5th rowCalifornia
ValueCountFrequency (%)
california 1371023
35.2%
alaska 797244
20.5%
nevada 177078
 
4.5%
hawaii 125336
 
3.2%
washington 82385
 
2.1%
islands 69799
 
1.8%
utah 57203
 
1.5%
montana 53899
 
1.4%
indonesia 48502
 
1.2%
puerto 47879
 
1.2%
Other values (597) 1064227
27.3%
2024-07-01T14:50:31.647512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6036019
18.6%
i 3677804
11.3%
3272283
10.1%
l 2467807
 
7.6%
n 2326249
 
7.2%
o 2023837
 
6.2%
r 1758548
 
5.4%
C 1493421
 
4.6%
f 1421221
 
4.4%
s 1236961
 
3.8%
Other values (53) 6815374
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32529524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6036019
18.6%
i 3677804
11.3%
3272283
10.1%
l 2467807
 
7.6%
n 2326249
 
7.2%
o 2023837
 
6.2%
r 1758548
 
5.4%
C 1493421
 
4.6%
f 1421221
 
4.4%
s 1236961
 
3.8%
Other values (53) 6815374
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32529524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6036019
18.6%
i 3677804
11.3%
3272283
10.1%
l 2467807
 
7.6%
n 2326249
 
7.2%
o 2023837
 
6.2%
r 1758548
 
5.4%
C 1493421
 
4.6%
f 1421221
 
4.4%
s 1236961
 
3.8%
Other values (53) 6815374
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32529524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6036019
18.6%
i 3677804
11.3%
3272283
10.1%
l 2467807
 
7.6%
n 2326249
 
7.2%
o 2023837
 
6.2%
r 1758548
 
5.4%
C 1493421
 
4.6%
f 1421221
 
4.4%
s 1236961
 
3.8%
Other values (53) 6815374
21.0%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct733599
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-101.28763
Minimum-179.9997
Maximum180
Zeros17
Zeros (%)< 0.1%
Negative3066668
Negative (%)89.0%
Memory size26.3 MiB
2024-07-01T14:50:31.784141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-179.9997
5-th percentile-162.7131
Q1-146.4274
median-118.95383
Q3-115.92767
95-th percentile126.364
Maximum180
Range359.9997
Interquartile range (IQR)30.499733

Descriptive statistics

Standard deviation76.974157
Coefficient of variation (CV)-0.75995615
Kurtosis4.6265558
Mean-101.28763
Median Absolute Deviation (MAD)6.59
Skewness2.3316125
Sum-3.4901195 × 108
Variance5925.0208
MonotonicityNot monotonic
2024-07-01T14:50:31.949208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.8103333 784
 
< 0.1%
-122.7963333 703
 
< 0.1%
-117.68 621
 
< 0.1%
-122.8125 617
 
< 0.1%
-116.468 583
 
< 0.1%
-116.465 573
 
< 0.1%
-116.462 573
 
< 0.1%
-116.47 573
 
< 0.1%
-116.466 568
 
< 0.1%
-116.467 568
 
< 0.1%
Other values (733589) 3439588
99.8%
ValueCountFrequency (%)
-179.9997 1
 
< 0.1%
-179.999 6
< 0.1%
-179.9989 1
 
< 0.1%
-179.9987 1
 
< 0.1%
-179.9986 1
 
< 0.1%
-179.9985 3
< 0.1%
-179.9983 2
 
< 0.1%
-179.9982 2
 
< 0.1%
-179.998 4
< 0.1%
-179.9978 1
 
< 0.1%
ValueCountFrequency (%)
180 8
< 0.1%
179.9999 2
 
< 0.1%
179.9994 1
 
< 0.1%
179.9993 4
< 0.1%
179.999 8
< 0.1%
179.9989 1
 
< 0.1%
179.9988 1
 
< 0.1%
179.9986 1
 
< 0.1%
179.9983 1
 
< 0.1%
179.9982 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct518295
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.46483
Minimum-84.422
Maximum87.386
Zeros9
Zeros (%)< 0.1%
Negative223047
Negative (%)6.5%
Memory size26.3 MiB
2024-07-01T14:50:32.103848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-84.422
5-th percentile-7.158
Q134.064
median37.935667
Q347.848
95-th percentile63.3374
Maximum87.386
Range171.808
Interquartile range (IQR)13.784

Descriptive statistics

Standard deviation20.415765
Coefficient of variation (CV)0.54493148
Kurtosis4.0482301
Mean37.46483
Median Absolute Deviation (MAD)4.7436667
Skewness-1.6189768
Sum1.2909448 × 108
Variance416.80348
MonotonicityNot monotonic
2024-07-01T14:50:32.271805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.8216667 1186
 
< 0.1%
38.8195 975
 
< 0.1%
38.8146667 868
 
< 0.1%
38.8356667 864
 
< 0.1%
38.8218333 819
 
< 0.1%
38.8125 790
 
< 0.1%
38.8223333 774
 
< 0.1%
38.8221667 770
 
< 0.1%
38.823 763
 
< 0.1%
38.8211667 760
 
< 0.1%
Other values (518285) 3437182
99.8%
ValueCountFrequency (%)
-84.422 1
< 0.1%
-84.133 1
< 0.1%
-83.902 1
< 0.1%
-82.8837 1
< 0.1%
-82.064 1
< 0.1%
-81.17 1
< 0.1%
-80.732 1
< 0.1%
-79.9837 1
< 0.1%
-77.36 1
< 0.1%
-77.134 1
< 0.1%
ValueCountFrequency (%)
87.386 1
< 0.1%
87.3752 1
< 0.1%
87.092 1
< 0.1%
87.081 1
< 0.1%
87.051 1
< 0.1%
87.009 1
< 0.1%
87.008 1
< 0.1%
86.9964 1
< 0.1%
86.986 1
< 0.1%
86.979 1
< 0.1%

depth
Real number (ℝ)

ZEROS 

Distinct78386
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.853874
Minimum-10
Maximum735.8
Zeros63714
Zeros (%)1.8%
Negative163316
Negative (%)4.7%
Memory size26.3 MiB
2024-07-01T14:50:32.442647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q13.12
median7.7
Q316.12
95-th percentile100
Maximum735.8
Range745.8
Interquartile range (IQR)13

Descriptive statistics

Standard deviation54.849379
Coefficient of variation (CV)2.4000036
Kurtosis56.921385
Mean22.853874
Median Absolute Deviation (MAD)5.34
Skewness6.6779152
Sum78748759
Variance3008.4544
MonotonicityNot monotonic
2024-07-01T14:50:32.597126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 170770
 
5.0%
0 63714
 
1.8%
33 55936
 
1.6%
5 39390
 
1.1%
35 25132
 
0.7%
1 16950
 
0.5%
7 9318
 
0.3%
8 8296
 
0.2%
6 8087
 
0.2%
15 8057
 
0.2%
Other values (78376) 3040101
88.2%
ValueCountFrequency (%)
-10 5
< 0.1%
-6.8 1
 
< 0.1%
-5.6 1
 
< 0.1%
-4.7 1
 
< 0.1%
-4.6 1
 
< 0.1%
-4.5 1
 
< 0.1%
-3.9 1
 
< 0.1%
-3.882 1
 
< 0.1%
-3.816 1
 
< 0.1%
-3.812 1
 
< 0.1%
ValueCountFrequency (%)
735.8 1
 
< 0.1%
721.8 1
 
< 0.1%
712.5 1
 
< 0.1%
712.2 1
 
< 0.1%
709.7 1
 
< 0.1%
700.9 1
 
< 0.1%
700.5 1
 
< 0.1%
700 7
< 0.1%
699 1
 
< 0.1%
698.1 1
 
< 0.1%

date
Text

Distinct3428775
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size26.3 MiB
2024-07-01T14:50:34.320014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length32
Mean length31.904616
Min length25

Characters and Unicode

Total characters109935361
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3411804 ?
Unique (%)99.0%

Sample

1st row1990-01-01 00:22:33.990000+00:00
2nd row1990-01-01 00:24:51.210000+00:00
3rd row1990-01-01 00:34:43.450000+00:00
4th row1990-01-01 00:58:32.130000+00:00
5th row1990-01-01 01:03:44.490000+00:00
ValueCountFrequency (%)
2008-04-26 2587
 
< 0.1%
2008-04-27 2373
 
< 0.1%
2008-04-28 1747
 
< 0.1%
2020-10-01 1486
 
< 0.1%
2014-09-26 1449
 
< 0.1%
2008-04-25 1437
 
< 0.1%
2018-12-01 1383
 
< 0.1%
2008-05-03 1359
 
< 0.1%
2002-11-10 1326
 
< 0.1%
2021-06-06 1297
 
< 0.1%
Other values (3090933) 6875058
99.8%
2024-07-01T14:50:35.811032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39338567
35.8%
: 10337253
 
9.4%
1 9691993
 
8.8%
2 9403006
 
8.6%
- 6891502
 
6.3%
3 4161835
 
3.8%
9 4057174
 
3.7%
4 3859638
 
3.5%
5 3843479
 
3.5%
3445751
 
3.1%
Other values (5) 14905163
 
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109935361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39338567
35.8%
: 10337253
 
9.4%
1 9691993
 
8.8%
2 9403006
 
8.6%
- 6891502
 
6.3%
3 4161835
 
3.8%
9 4057174
 
3.7%
4 3859638
 
3.5%
5 3843479
 
3.5%
3445751
 
3.1%
Other values (5) 14905163
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109935361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39338567
35.8%
: 10337253
 
9.4%
1 9691993
 
8.8%
2 9403006
 
8.6%
- 6891502
 
6.3%
3 4161835
 
3.8%
9 4057174
 
3.7%
4 3859638
 
3.5%
5 3843479
 
3.5%
3445751
 
3.1%
Other values (5) 14905163
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109935361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39338567
35.8%
: 10337253
 
9.4%
1 9691993
 
8.8%
2 9403006
 
8.6%
- 6891502
 
6.3%
3 4161835
 
3.8%
9 4057174
 
3.7%
4 3859638
 
3.5%
5 3843479
 
3.5%
3445751
 
3.1%
Other values (5) 14905163
 
13.6%

Interactions

2024-07-01T14:50:16.621593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:02.293353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:04.629205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:06.849291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:09.290920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:11.859290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:14.188943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:16.988471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:02.616751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:04.951749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:07.195981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:09.637513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:12.214565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:14.516971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:17.347722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:02.960854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:05.274415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:07.514138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:10.022438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:12.577297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:14.909691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:17.747245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:03.277725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:05.587487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:07.836430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:10.375641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:12.892666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:15.234791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:18.112736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:03.615535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:05.896366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:08.172572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:10.761114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:13.216667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:15.620939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:18.456342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:03.918221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:06.210164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:08.570503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:11.139206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:13.543789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:15.938967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:18.764829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:04.307855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:06.525239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:08.909684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:11.494110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:13.848873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-01T14:50:16.288254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-01T14:50:35.913801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
depthlatitudelongitudemagnitudosignificancetimetsunami
depth1.000-0.0170.0160.4090.4080.0890.018
latitude-0.0171.000-0.561-0.248-0.2480.1310.005
longitude0.016-0.5611.0000.2330.233-0.175-0.005
magnitudo0.409-0.2480.2331.0001.000-0.1470.034
significance0.408-0.2480.2331.0001.000-0.1460.035
time0.0890.131-0.175-0.147-0.1461.0000.019
tsunami0.0180.005-0.0050.0340.0350.0191.000

Missing values

2024-07-01T14:50:19.303355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-01T14:50:21.009330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timeplacestatustsunamisignificancedata_typemagnitudostatelongitudelatitudedepthdate
063115335399012 km NNW of Meadow Lakes, Alaskareviewed096earthquake2.50Alaska-149.66920061.73020030.1001990-01-01 00:22:33.990000+00:00
163115349121014 km S of Volcano, Hawaiireviewed031earthquake1.41Hawaii-155.21233319.3176676.5851990-01-01 00:24:51.210000+00:00
26311540834507 km W of Cobb, Californiareviewed019earthquake1.11California-122.80616738.8210003.2201990-01-01 00:34:43.450000+00:00
363115551213011 km E of Mammoth Lakes, Californiareviewed015earthquake0.98California-118.84633337.664333-0.5841990-01-01 00:58:32.130000+00:00
463115582449016km N of Fillmore, CAreviewed0134earthquake2.95California-118.93400034.54600016.1221990-01-01 01:03:44.490000+00:00
563115585376016km N of Fillmore, CAreviewed0118earthquake2.77California-118.92300034.54300016.3421990-01-01 01:04:13.760000+00:00
66311560305706 km WSW of Mammoth Lakes, Californiareviewed020earthquake1.13California-119.04000037.632667-1.4991990-01-01 01:07:10.570000+00:00
763115643195012 km E of Mammoth Lakes, Californiareviewed011earthquake0.83California-118.83416737.6615000.5561990-01-01 01:13:51.950000+00:00
863115661407011 km E of Mammoth Lakes, Californiareviewed039earthquake1.59California-118.84016737.662333-0.2341990-01-01 01:16:54.070000+00:00
96311566202101 km W of Wilkeson, Washingtonreviewed074earthquake2.20Washington-122.07116747.1025008.3821990-01-01 01:17:00.210000+00:00
timeplacestatustsunamisignificancedata_typemagnitudostatelongitudelatitudedepthdate
344574116906253100204 km SW of Redlands, CAautomatic016earthquake1.01California-117.21550034.03016714.4502023-07-29 10:08:30.020000+00:00
34457421690625393345Southern Alaskaautomatic022earthquake1.20Southern Alaska-152.15940060.10490053.9002023-07-29 10:09:53.345000+00:00
3445743169062616440016 km ESE of Julian, CAautomatic011earthquake0.85California-116.43283333.0358337.3702023-07-29 10:22:44.400000+00:00
3445744169062669910287 km NNW of Karluk, Alaskaautomatic015earthquake1.00Alaska-155.20450058.2413000.0002023-07-29 10:31:39.102000+00:00
344574516906268159800 km SW of Universal City, CAautomatic016earthquake1.03California-118.35683334.13550015.7102023-07-29 10:33:35.980000+00:00
344574616906268519415 km NW of Chikusei, Japanreviewed0326earthquake4.60Japan139.94020036.35070083.0392023-07-29 10:34:11.941000+00:00
34457471690626975715Kodiak Island region, Alaskaautomatic044earthquake1.70Alaska-153.72990057.79010024.4002023-07-29 10:36:15.715000+00:00
3445748169062721594012 km W of Alberto Oviedo Mota, B.C., MXautomatic090earthquake2.42B.C.-115.29683332.2331671.7702023-07-29 10:40:15.940000+00:00
344574916906281460407 km W of Cobb, CAautomatic016earthquake1.03California-122.80049938.8274991.7202023-07-29 10:55:46.040000+00:00
3445750169062893788435 km W of Karluk, Alaskaautomatic012earthquake0.90Alaska-155.05100057.564800250.0002023-07-29 11:08:57.884000+00:00

Duplicate rows

Most frequently occurring

timeplacestatustsunamisignificancedata_typemagnitudostatelongitudelatitudedepthdate# duplicates
15193164610899380554 km S of Whites City, New Mexicoreviewed062earthquake2.00New Mexico-104.38663031.6826426.1315672022-03-01 04:29:53.805000+00:004
16014164626454344955 km S of Whites City, New Mexicoreviewed081earthquake2.30New Mexico-104.40192931.6777177.2884282022-03-02 23:42:23.449000+00:004
06362496546503 km E of Mammoth Lakes, Californiareviewed042earthquake1.66California-118.93366737.6470003.8160001990-03-01 00:00:54.650000+00:002
16362496678904km N of Claremont, CAreviewed070earthquake2.13California-117.71700034.1330004.2750001990-03-01 00:01:07.890000+00:002
26362496944004km N of Claremont, CAreviewed062earthquake2.00California-117.71700034.1330004.2750001990-03-01 00:01:34.400000+00:002
36362497124005km NNE of Claremont, CAreviewed022earthquake1.20California-117.70500034.1440005.7020001990-03-01 00:01:52.400000+00:002
46362497241704km N of Claremont, CAreviewed0160earthquake3.22California-117.71400034.1300005.6220001990-03-01 00:02:04.170000+00:002
56362497289704km NNE of Claremont, CAreviewed0149earthquake3.11California-117.70900034.1340003.8420001990-03-01 00:02:08.970000+00:002
66362497515704km NNE of Claremont, CAreviewed0110earthquake2.68California-117.70900034.1320003.3720001990-03-01 00:02:31.570000+00:002
76362497952804km NNE of Claremont, CAreviewed0108earthquake2.65California-117.70600034.1330003.3820001990-03-01 00:03:15.280000+00:002